which neglects second order derivatives of \(f\)  w.r.t. the parameters. Besides, the computational benefits of this approximations, previous works on la [1]} relies on ggn to ensure that the Hessian is always semi-negative definite. In contrast, the model presented in sec:method implies that ggn is no longer a practical and unprincipled trick, but rather the exact Hessian.
